Data Science Fellowship

Overview

The Institute for Defense Analyses (IDA) is a not-for-profit corporation that operates three Federally Funded Research & Development Centers (FFRDCs). IDA's Systems and Analyses Center is located in Alexandria, Virginia and is comprised of seven research divisions providing objective analyses of national security issues, particularly those requiring scientific and technical expertise, and conducts related research on other national challenges.

The IDA Data Science Fellowship provides recent graduates a unique opportunity to develop and apply data science skills to important issues in national security. The Data Science Fellowship is a project-based learning experience within a variety of research areas. In a collaborative team environment, Fellows perform data manipulation and statistical, econometric, predictive, descriptive, and other quantitative analyses to help answer important sponsor-funded research questions as well as internal-funded business operations questions.

In the course of research, Fellows will apply advanced data science tools, possibly including machine learning, artificial intelligence, statistics, or various big data methodologies. Fellows should expect to learn while using their critical thinking, creativity, and analytic skills to contribute to interdisciplinary project teams.

Fellows will have opportunity to work on several research questions during their 3-year terms. Specific future research projects are unpredictable, subject to ever-changing analytical needs of sponsors and/or internal business operations questions. However, example projects include:

• Appraisal of current Department of War (DoW) investments in a broad range of areas: from human factors to autonomous systems, from materials science to nuclear weapons effects, from social behaviors to quantum computing.
• Analyze and research questions on DoW personnel, military readiness and efficacy, and organizational efficiency topics.
• Assessment of federal agency information and computing architectures that support data science applications (i.e., large, distributed data sets and computational assets).
• Application of data exploration, text analytics, forecasting, statistical inference, simulation to areas of military personnel, manpower, and acquisition of DoW weapon systems.
• Improve IDA's data architecture to support efficient internal operations.
• Analyze financial information to surface insights about research project execution across the company and inform decision‑making.

Over the course of the three‑year program, fellowship experiences will include:

• Involvement in workshops and discussions on relevant topics.
• Mentorship from members of the IDA research staff.
• Training on specific analytical methods and tools.
• Attendance and presentation at professional society and/or academic meetings/conferences.

• This is a full‑time position and is only open to recent recipients of a bachelor's or master's degree. Candidates with degrees higher than a master's degree will not be considered.
• Candidates with recent Bachelor's degree or Masters' degree in economics, statistics, operations research, mathematics, physics, computer science, data science, data engineering, electrical engineering, neuroscience, political science or other disciplines with a strong foundation in statistics and/or applied mathematics are encouraged to apply.
• Candidates must demonstrate experience with one or more programming languages or statistical software used in IDA's research (e.g., Python, R, Julia, Stata, MATLAB, C, Java, SQL, SPARQL, etc.).
• Candidates must demonstrate strong written and oral communication skills.
• Ideal candidates are able to contribute to and support team efforts.
• Additional preferred skills, including one or more of:
  • Training and/or experience in quantitative or qualitative information collection, data normalization, or text analytics
  • Experience as a research assistant in an academic or policy research setting
  • Experience in data engineering and/or database management
  • Experience or coursework in Bayesian statistics, machine learning, predictive analytics, and/or geospatial analyses
  • Experience with knowledge graphs or graph…